Sovereign AI
Your AI. Your data. Your infrastructure.
Open models deployed on controlled infrastructure — private cloud, on-premise or disconnected — with the full data flow and operating responsibilities defined and tested.
When it wins
Where sovereign deployment is the answer
Three situations where data must not leave your walls.
Regulated sectors
Banking, insurance, health, public sector: when the regulator mandates data residency or rules out public cloud, sovereign deployment is the compliance path.
Critical data
Trade secrets, defence data, patient records: some information must never pass through a third-party API, whatever the level of encryption.
Strategic independence
You choose your models, your hardware and your update schedule — without depending on a single vendor's roadmap or terms.
Models
Supported open models
Model choice is never a matter of fashion: it is measured on your data, your hardware and your licence constraints.
Quality on your use cases
Every model is evaluated on your real documents and tasks — not on generic benchmarks — before we settle on a choice.
Hardware footprint
Model size, quantization and expected throughput drive GPU sizing: we calibrate the model-hardware pairing precisely.
Licence & longevity
Commercial usage terms, tooling dependencies and the ability to evaluate updates are reviewed alongside the model's capabilities.
FAQ
Frequently asked
Is sovereign AI less capable than the cloud?
There is no universal answer. Evaluate quality, latency and the complete retrieval and tool workflow on your approved tasks and available hardware before comparing it with a cloud service.
How much does a sovereign AI deployment cost?
The assessment includes hardware or hosting, integration, storage, support, updates and operating staff. Usage volume and capacity requirements determine the comparison with cloud services; a lower cost is not assumed.
Who maintains and updates the models?
Hunter BI operates the system or transfers the skill to your teams, your choice: monitoring, version upgrades evaluated on private test benches before production, and full operations documentation. You keep control of the schedule.
Does sovereign AI mean fully disconnected?
No. Air-gapping — total isolation — is only the strictest level, reserved for the most sensitive data. A dedicated private cloud or an on-premise deployment keeps controlled connectivity: managed updates, governed remote access, full logging.
Define what sovereignty means for your project
Start with the information that must remain under your organisation's control and the reasons for that requirement. The scope includes inference, document retrieval, embedding services, connectors, logs, monitoring and administrative access. A locally hosted model does not make every dependency local.
For public institutions and businesses in Morocco, the target should be stated as testable requirements: approved hosting, authorised administrators, allowed network destinations and an update process. Security and legal stakeholders validate those requirements. The term sovereign is not a substitute for describing the complete system.
Private cloud, on-premise and disconnected operation
These deployment modes have different operational implications. A private environment can retain controlled external connectivity; a disconnected environment requires a specific process for importing updates and operating without external services. Neither option should be selected from a marketing label alone.
We assess the models, licences, available hardware and workload together. Representative documents and tasks reveal the quality and latency that the actual environment can deliver. Commercial model services and locally deployed models should not be presented as interchangeable without testing.
Plan the cost and the operating responsibilities
The cost assessment includes hardware or hosting, integration, storage, monitoring, updates and the people needed to operate the service. Local inference is not automatically cheaper than a cloud API. Usage volume, peak demand and support requirements change the comparison.
Before commissioning, agree an acceptance set, data-flow checks and a procedure to suspend the service. Record the tested versions and review changes before production. Your organisation keeps a clear owner for security, capacity and incidents; the contract specifies which responsibilities Hunter BI takes on and which remain internal.
Keep your AI inside your walls?
Private cloud, on-premise or air-gapped: let's scope a sovereign deployment on your data.